A cognitive radio system operates in frequency channels that are licensed for a specific communication service. This specific communication service is typically referred to as the incumbent service, and existing users of the incumbent service are denoted as incumbent users or primary users. A user of the cognitive radio system may be referred to as a secondary user.
In a cognitive radio system, spectrum sensing is a crucial operation for the secondary user (SU) to detect whether a frequency band or a frequency channel licensed to the incumbent radio service is used by a primary user (PU). Spectrum sensing methods can be broadly categorized into three categories: matched filtering methods, energy detection methods, and cyclostationarity detection methods. Each method has its own advantages and disadvantages.
In case that all information about a signal that may be or may not be transmitted by a primary user (and for which it is to be detected whether it is currently transmitted) is known a priori, matched filtering is the optimum detection method (i.e. the optimum spectrum sensing method). However, since this method requires perfect knowledge of the primary user's transmission parameters such as used bandwidth, modulation type, pulse shaping method, frame format etc., the complexity of this method is considerably high for implementation.
For spectrum sensing based on energy detection, the primary user's transmission parameters are not required. Spectrum sensing methods based on energy detection have therefore the least complexity among the three spectrum sensing method categories. However, energy detection spectrum sensing methods are typically vulnerable to noise uncertainty. Inaccurate estimation of the noise power can be the reason for a SNR (signal to noise ratio) wall for the detection and to a high false alarm probability.
Recently, spectrum sensing based on cyclostationarity detection has attracted substantial interest due to its ability to distinguish among wireless systems that transmit signals having cyclostationarity. For example, wireless systems using different parameters such as different modulation types, symbol rates, carrier frequencies etc. show cyclostationary features at different cycle frequencies and may thus be distinguished. Signal detection for spectrum sensing can be performed by checking the presence of cyclostationary features at these cycle frequencies.
Since the spectrum sensing method based on cyclostationarity detection can be used for distinguishing between a signal transmitted by a primary user and interfering signals it may be beneficial for coexistence scenarios in cognitive radio which have been investigated for standardization.
It is therefore desirable to provide improved methods for spectrum sensing, in particular based on cyclostationarity detection.